Accurate prediction of ATP-binding residues using sequence and sequence-derived structural descriptors

ATP is a ubiquitous nucleotide that provides energy for cellular activities, catalyzes chemical reactions, and is involved in cellular signaling. The knowledge of the ATPprotein interactions helps with annotation of protein functions and finds applications in drug design. We propose a highthroughput machine learning-based predictor, ATPsite, which identifies ATP-binding residues from protein sequences. Statistical tests show that ATPsite significantly outperforms existing ATPint predictor and other solutions which utilize sequence alignment and residue conservation scoring. The improvements stem from the usage of novel custom-designed input features that are based on the sequence, evolutionary profiles, and the sequence-predicted structural descriptors including secondary structure, solvent accessibility, and dihedral angles. A simple consensus of the ATPsite with the sequence-alignment based predictor is shown to give further improvements. Keywords - ATP binding; protein-ATP interacti...

• Type: Journal
• Year: 2010
• Where: BIBM
• Authors: Ke Chen 0003, Marcin J. Mizianty, Lukasz A. Kurgan
• Keywords: ATPint Predictor, BIBM 2010, Bioinformatics, Machine Learning-based Predictor, Sequence-predicted Structural Descriptors